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Accelerating the evolutionary-gradient-search procedure: Individual step sizes

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Parallel Problem Solving from Nature — PPSN V (PPSN 1998)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1498))

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Abstract

Recent research has proposed the evolutionary-gradient-search procedure that uses the evolutionary scheme to estimate a gradient direction and that performs the parameter updates in a steepest-descent form. On several test functions, the procedure has shown faster convergence than other evolutionary algorithms. However, the procedure also exhibits similar deficiencies as steepest-descent methods. This paper explores to which extent the adoption of individual step sizes, as known from evolution strategies, can be beneficially used. It turns out that they considerably accelerate convergence.

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References

  1. Bäck, T., Schwefel, H.-P.: An Overview of Evolutionary Algorithms for Parameter Optimization. Evolutionary Computation 1(1) (1993) 1–23

    Google Scholar 

  2. Bäck, T., Kursawe, F.: Evolutionary Algorithms for Fuzzy Logic: A Brief Overview. In: Bouchon-Meunier, B., Yager, R.R., Zadeh, L.A. (eds.): Fuzzy Logic and Soft Computing, Vol. IV. World Scientific, Singapore (1995) 3–10

    Google Scholar 

  3. Beyer, H.-G.: An Alternative Explanation for the Manner in which Genetic Algorithms Operate. BioSystems 41 (1997) 1–15

    Article  Google Scholar 

  4. Fogel, D.B.: Evolutionary Computation: Toward a New Philosophy of Machine Learning Intelligence. IEEE Press, Jersy, NJ (1995)

    Google Scholar 

  5. Fogel, L.J.: “Autonomous Automata”. Industrial Research 4 (1962) 14–19

    Google Scholar 

  6. Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley Publishing Company (1989)

    Google Scholar 

  7. Hansen, N., Ostermeier, A.: Adapting Arbitrary Normal Mutation Distributions in Evolution Strategies: The Covariance Matrix Adaptation. In: Proceedings of The 1996 IEEE International Conference on Evolutionary Computation (IECEC'96). IEEE (1996) 312–317

    Google Scholar 

  8. Hansen, N., Ostermeier, A.: Convergence Properties of Evolution Strategies with the Derandomized Covariance Matrix Adaptation: The (Μ/Μ I, λ)-CMA-ES. In: Zimmermann, H.-J. (ed.): Proceedings of The Fifth Congress on Intelligent Techniques and Soft Computing EUFIT'97. Verlag Mainz, Achen, (1997) 650–654

    Google Scholar 

  9. Luenberger, D.G.: Linear and Nonlinear Programming. Addison-Wesley, Menlo Park, CA (1984)

    Google Scholar 

  10. Mühlenbein, H., Schlierkamp-Voosen, D.: Predictive Models for the Breeder Genetic Algorithm I. Evolutionary Computation 1(1) (1993) 25–50.

    Google Scholar 

  11. Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C. Cambridge University Press, Cambridge, UK (1994)

    Google Scholar 

  12. Rechenberg, I.: Evolutionsstrategie. Frommann-Holzboog, Stuttgart (1994)

    Google Scholar 

  13. Rumelhart et al. (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 2. The MIT Press, Cambridge, MA (1986)

    Google Scholar 

  14. Salomon, R.: Reevaluating Genetic Algorithm Performance under Coordinate Rotation of Benchmark Functions; A survey of some theoretical and practical aspects of genetic algorithms. BioSystems 39(3) (1996) 263–278

    Article  Google Scholar 

  15. Salomon, R.: The Evolutionary-Gradient-Search Procedure. In: Koza, J. et al. (eds.): Genetic Programming 1998: Proceedings of the Third Annual Conference, July 22–25, 1998. Morgan Kaufmann, San Francisco, CA (1998)

    Google Scholar 

  16. Salomon, R., van Hemmen, J.L.: Accelerating backpropagation through dynamic self-adaptation. Neural Networks 9(4) (1996) 589–601

    Article  Google Scholar 

  17. Schwefel, H.-P.: Evolution and Optimum Seeking. John Wiley and Sons, NY (1995)

    Google Scholar 

  18. Schwefel, H.-P.: Evolutionary Computation — A Study on Collective Learning. In: Callaos, N., Khoong, C.M., Cohen, E. (eds.): Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics, vol. 2. Int'l Inst. of Informatics and Systemics, Orlando FL (1997) 198–205

    Google Scholar 

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Authors and Affiliations

  1. AI Lab, Department of Computer Science, University of Zurich, Winterthurerstrasse 190, 8057, Zurich, Switzerland

    Ralf Salomon

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  1. Ralf Salomon
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Editor information

Agoston E. Eiben Thomas Bäck Marc Schoenauer Hans-Paul Schwefel

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© 1998 Springer-Verlag Berlin Heidelberg

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Salomon, R. (1998). Accelerating the evolutionary-gradient-search procedure: Individual step sizes. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN V. PPSN 1998. Lecture Notes in Computer Science, vol 1498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056883

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  • DOI: https://doi.org/10.1007/BFb0056883

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65078-2

  • Online ISBN: 978-3-540-49672-4

  • eBook Packages: Springer Book Archive

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